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Simultaneous Wire Permutation, Inversion, and Spacing with Genetic Algorithm for Energy-Efficient Bus Design

机译:具有遗传算法的同步电线置换,反转和间距,用于节能总线设计

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With decreasing feature size on silicon, the coupling capacitances of buses grow rapidly causing a significant impact on the power consumption of the whole chip. Thus, buses should be designed and optimized to dissipate less power without sacrificing performance. In this paper, we address this problem by simultaneously optimizing wire permutation, inversion and spacing (space between consecutive wires) using a combination of optimal as well as genetic algorithms. Unlike previous studies, our approach is applicable to not only address buses (behave more regularly), but also instruction buses of microprocessors. For the spacing problem, an algorithm is presented which determines the optimal solution instead of applying time consuming heuristic algorithms as presented in [10]. For our experiments, we used instruction bus traces obtained from 12 SPEC2000 benchmark programs. We simulate different combinations among permutation, spacing, and inversion. Integrated all optimization techniques together, our approach can save energy up to 68% for the best case and 58% on average while only increasing the total wire space by about 50% (compared to a bus with minimal spacing between adjacent wires for a particular technology).
机译:随着硅上的特征尺寸减小,总线的耦合电容迅速增长,对整个芯片的功耗产生重大影响。因此,应设计公共汽车并优化,以在不牺牲性能的情况下耗散更少的功率。在本文中,我们通过使用最佳以及遗传算法的组合同时优化导线置换,反转和间距(连续电线之间的空间)来解决该问题。与之前的研究不同,我们的方法不仅适用于地址公共汽车(更定期表现),还适用于微处理器的指令总线。对于间隔问题,提出了一种算法,其确定最佳解决方案,而不是应用[10]所示的耗时的启发式算法。对于我们的实验,我们使用了从12个规格2000基准程序获得的指令总线迹线。我们在排列,间隔和反转之间模拟不同的组合。将所有优化技术集成在一起,我们的方法可以节省最佳案例的能量高达68%,而平均仅58%,同时仅将总线空间增加约50%(与具有特定技术相邻电线之间最小间隔最小的总线相比增加了总线空间)。

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